How do I stop it from using the same five cliche phrases?

I've been conducting a series of standardized tests on Copy.ai's long-form article generator over the past several weeks, using a controlled set of input prompts across different content categories (technology introductions, product descriptions, blog outlines). My primary performance metric isn't raw speed, but rather **lexical diversity** and **phrase repetition** across outputs.

A consistent and statistically significant flaw has emerged: the model exhibits a pronounced tendency to default to a specific set of high-frequency, low-information clichés. This isn't just anecdotal; my log analysis shows the same phrases appearing with a frequency that suggests an over-reliance on certain trained patterns rather than generative novelty.

The most common offenders in my results include:
* "In today's fast-paced digital world..."
* "It's no secret that..."
* "The bottom line is..."
* "Leverage the power of..."
* "Take your [X] to the next level..."

This creates a bottleneck in production quality. If I generate ten articles on different subtopics, they all start to sound homogenized, which fails my basic benchmark for usable, distinct content.

I've attempted to mitigate this by refining my prompts with explicit instructions. My test protocol involves iterative prompt engineering. For example:

**Baseline Prompt:**
```
Write a 500-word article about the benefits of using a project management tool for remote teams.
```

**Engineered Prompt (Attempt 1):**
```
Write a 500-word article for experienced project managers about the concrete operational benefits of using a project management tool for distributed remote teams. Avoid generic phrases like "fast-paced world" or "take it to the next level." Use specific, actionable language.
```

While the engineered prompt sometimes reduces the occurrence of the top-tier clichés, it often simply substitutes them with secondary overused phrases ("In the contemporary landscape," "unlock potential"). The instruction to "avoid generic phrases" is not consistently adhered to.

My question for the community is methodological: what prompt architectures or workflow configurations have you found that reliably break this pattern?

* Are there specific **style modifiers** (e.g., "write in the style of a technical white paper," "adopt a skeptical tone") that force the engine out of its default phrasing?
* Is there a **prefacing system prompt** or a particular way to structure the initial instructions that acts as a more effective constraint?
* Have you found that using the **"Improve"** or **"Rephrase"** tools on the initial output with a focus solely on phrase replacement yields a better result than trying to prevent it in the first generation?

I am looking for reproducible, testable strategies. I will run any proposed methods through my standard battery of ten fixed prompts and report back on the percentage reduction in target cliché phrases. My current baseline shows cliché occurrence in 80% of outputs; a solution that brings this below 20% would be considered a significant performance improvement.

-- bb42